40 citations · 66 across the 12 of their papers we have counts for
12 papers
Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit
Adrian Celaya, Tucker Netherton, Dawid Schellingerhout +3
Medical image segmentation continues to advance rapidly, yet rigorous comparison between methods remains challenging due to a lack of standardized and customizable tooling. In this…
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
Adrian Celaya, Yimo Wang, David Fuentes +1
In recent years, there has been an increasing interest in using deep learning and neural networks to tackle scientific problems, particularly in solving partial differential equati…
Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
Adrian Celaya, David Fuentes, Beatrice Riviere
Physics-informed neural networks (PINNs) have gained significant attention for solving forward and inverse problems related to partial differential equations (PDEs). While advancem…
Deep Learning-Based Automated Post-Operative Gross Tumor Volume Segmentation in Glioblastoma Patients
Rajarajeswari Muthusivarajan, Adrian Celaya, Maguy Farhat +8
Precise automated delineation of post-operative gross tumor volume in glioblastoma cases is challenging and time-consuming owing to the presence of edema and the deformed brain tis…
MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework
Adrian Celaya, Evan Lim, Rachel Glenn +7
Medical imaging segmentation is a highly active area of research, with deep learning-based methods achieving state-of-the-art results in several benchmarks. However, the lack of st…
Solutions to Elliptic and Parabolic Problems via Finite Difference Based Unsupervised Small Linear Convolutional Neural Networks
Adrian Celaya, Keegan Kirk, David Fuentes +1
In recent years, there has been a growing interest in leveraging deep learning and neural networks to address scientific problems, particularly in solving partial differential equa…
Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO Plumes via Deep Learning
Adrian Celaya, Mauricio Araya-Polo
We introduce a fully 3D, deep learning-based approach for the joint inversion of time-lapse surface gravity and seismic data for reconstructing subsurface density and velocity mode…
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation
Adrian Celaya, Beatrice Riviere, David Fuentes
Accurate medical imaging segmentation is critical for precise and effective medical interventions. However, despite the success of convolutional neural networks (CNNs) in medical i…
A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation
Adrian Celaya, Beatrice Riviere, David Fuentes
Within medical imaging segmentation, the Dice coefficient and Hausdorff-based metrics are standard measures of success for deep learning models. However, modern loss functions for…
Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO Plumes via Deep Learning
Adrian Celaya, Bertrand Denel, Yen Sun +2
We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. The first algorithm is a data-driven, deep learning-based approach, the seco…
Correlation between image quality metrics of magnetic resonance images and the neural network segmentation accuracy
Rajarajeswari Muthusivarajan, Adrian Celaya, Joshua P. Yung +4
Deep neural networks with multilevel connections process input data in complex ways to learn the information.A networks learning efficiency depends not only on the complex neural n…
PocketNet: A Smaller Neural Network for Medical Image Analysis
Adrian Celaya, Jonas A. Actor, Rajarajeswari Muthusivarajan +5
Medical imaging deep learning models are often large and complex, requiring specialized hardware to train and evaluate these models. To address such issues, we propose the PocketNe…